Ponding of meltwater on the surface of the Greenland Ice Sheet has the potential to reduce ice sheet albedo and amplify mass loss. However, this process remains poorly constrained and is absent from models that project ice sheet mass balance. Here we demonstrate that meltwater ponding considerably increases the amount of energy available for melting the Greenland Ice Sheet. We first use satellite-derived products to show that meltwater ponding has a significant impact on spatial albedo patterns, particularly in the lower percolation zone. We then use drone imagery to demonstrate that, in the upper ablation zone, there are thousands of narrow streams and small pools (<100 m²) that collectively account for >50% of the total meltwater area. These small meltwater features are not resolved by surface water maps derived from medium-resolution satellite imagery, signifying that the radiative effect of meltwater ponding is three to four times stronger than predicted by satellite-based approaches. Our findings therefore place lower bounds on the radiative effect of meltwater ponding that could be used to advocate for the inclusion of this process into models that forecast Greenland Ice Sheet's contribution to sea-level rise.

The contribution of Greenland Ice Sheet meltwater runoff to global sea-level rise is accelerating due to increased melting of its bare-ice ablation zone. There is growing evidence, however, that climate models overestimate runoff from this critical area of the ice sheet. Climate models traditionally assume that all bare-ice runoff enters the ocean, unlike porous firn, in which some meltwater is retained and/or refrozen. We used field measurements and numerical modeling to reveal that extensive retention and refreezing also occurs in bare glacier ice. We found that, from 2009 to 2018, meltwater refreezing in bare, porous glacier ice reduced runoff by an estimated 11-17 Gt a-1 in southwest Greenland alone, equivalent to 9-15% of this sector's annual meltwater runoff simulated by climate models. This mass retention explains evidence from prior studies of runoff overestimation on bare ice by current generation climate models and may represent an overlooked buffer on projected runoff increases. Inclusion of bare-ice retention and refreezing processes in climate models therefore has immediate potential to improve forecasts of ice sheet runoff and its contribution to sea-level rise.

Landfast sea ice that forms along the Arctic coastline is of great importance to coastal Alaskan communities. It provides a stable platform for transportation and traditional activities, protects the coastline from erosion, and serves as a critical habitat for marine mammals. Here we present a full assessment of landfast ice conditions across a continuous 7885 km length of the Alaska coastline over 2000–2022 using satellite imagery. We find that the maximum landfast ice extent, usually occurring in March, averaged 67 002 km ^2 during our study period: equivalent to 4% of the state’s land area. The maximum extent of landfast ice, however, exhibits considerable interannual variability, from a minimum of 29 871 km ^2 in 2019 to a maximum of 87 571 km ^2 in 2010. Likewise, the landfast ice edge position averages 22.9 km from the coastline but, at the community-scale, can range from 2.8 km (in Gambell) to 71.1 km (in Deering). Landfast ice breakup date averages 2 June but also varies considerably both between communities (3 May in Quinhagak to 24 July in Nuiqsut) and interannually. We identify a strong control of air temperature on breakup timing and use this relationship to project future losses of ice associated with Paris Climate Agreement targets. Under 2 °C of global air temperature warming, we estimate the average Alaskan coastal community will lose 19 days of ice, with the northernmost communities projected to lose 50 days or more. Overall, our results emphasize the highly localized nature of landfast ice processes and the vulnerability of coastal Arctic communities in a warming climate.

Greenland Ice Sheet meltwater runoff projections are essential for accurate forecasts of global sea-level rise. However, melt depends on complex interactions between climate warming, surface albedo, and clouds, which are challenging to simulate in models. Here we use satellite observations, climate reanalysis, and regional climate model data to constrain the amount of melt due to processes that lower surface albedo and reduce cloud cover. We find that cloud radiative forcing had a small impact on melt during 2002-2023. In contrast, the radiative forcing due to spatiotemporal variability in surface albedo accounted for 10.0 +/- 3.7% of total meltwater production. Moreover, we find that a feedback between climate warming and surface albedo amplifies melt by +7.8 +/- 1.6 Gt yr-1 K-1. Ice sheet models that do not sufficiently account for this radiative feedback will not only underestimate the current production of meltwater but will also increasingly underestimate it in the future. During 2002-2023, Greenland Ice Sheet meltwater production was minimally affected by cloud radiative forcing, while surface albedo variability contributed substantially, according to results from satellite observations, climate reanalysis, and regional climate models.

Greenland's marine- and land-terminating glaciers are retreating inland due to climate warming, reconfiguring the way the ice sheet interacts with its proglacial environment. Here we use three decades of satellite imagery to determine whether the ice-sheet margin is becoming more or less exposed to marine and lacustrine processes. During our 1990-2019 study period, we find that the length of ice-sheet perimeter in contact with the ocean shrank by 12.3 +/- 3.8% (196.2 +/- 10.4 km), due to the retreat of marine-terminating glaciers into narrower fjords. On the other hand, we find that the length of the ice-sheet perimeter in contact with freshwater lakes exhibited more divergent trends that is better explored at regional scales. The length of ice-lake boundaries increased in southwest, north and northwest Greenland but declined in southeast and central east Greenland. The magnitude of change we document during our study period leads us to conclude that the ice sheet is poised for further, substantial reconfiguration in the coming decades with consequences for the flux of fresh water, nutrients and primary productivity in Greenland's terrestrial and oceanic environment.

The Greenland Ice Sheet is a leading contributor to global sea-level rise because climate warming has enhanced surface meltwater runoff. Melt rates are particularly sensitive to air temperatures due to feedbacks with albedo. The primary melt-albedo feedback, fluctuation of seasonal snowlines, however, is determined not only by melt but also by antecedent snowfall which could delay the onset of dark glacier ice exposure. Here we investigate the role of snowfall versus air temperatures on ice sheet melt-albedo feedbacks using satellite remote sensing and atmospheric reanalysis data. We find several lines of evidence that snowline fluctuations are driven primarily by air temperatures and that snowfall is a secondary control. First, standardized linear regressions indicate that the timing of glacier ice exposure is nearly twice as sensitive to air temperatures than antecedent snowfall. Second, in 74% of the ablation zone by area, winter snowfall rates are not significantly correlated with winter air temperatures. This relationship implies that ice sheet melt due to climate warming is unlikely to be compensated by higher snowfall rates in the ablation zone. Third, we find no significant change in snowfall rates in the ablation zone during our 1981-2021 study period. Our findings demonstrate that snowfall is unlikely to reduce future ice sheet melt and that ice sheet meltwater runoff should be accurately predicted by air temperatures. Although given the importance of melt-albedo feedbacks, ice sheet models that parameterize albedo or are coupled with regional climate models are likely to provide the most accurate projections of mass loss. The Greenland Ice Sheet is currently losing mass because rates of mass loss (mainly due to surface meltwater runoff and iceberg discharge into the ocean) are higher than rates of mass gain (mainly due to snowfall). As the climate warms, rates of mass loss are expected to increase non-linearly because rising air temperatures darken the surface, leading to more solar energy absorption, and more melting. The main process responsible for surface darkening is the exposure of dark glacier ice due to Greenland's seasonally evolving snowline. The position of the snowline is determined not only by summer melt but also by the thickness of the snowpack that accumulates during winter. In this study, we investigated whether summer melt or snowpack thickness was more important for determining the timing of glacier ice exposure using satellite remote sensing and climate model outputs. We found that the glacier ice exposure is much more sensitive to air temperatures than snowfall and that snowfall did not change in the ablation zone during the 1981-2021 study period. Our findings imply that snowfall is unlikely to reduce ice sheet mass loss and that meltwater runoff from the ice sheet should be accurately predicted by air temperatures. We investigate the extent to which antecedent snowfall versus summer air temperatures controls melt-albedo feedbacksWe find that melt-albedo feedbacks are primarily driven by air temperatures and that antecedent snowfall is of secondary importanceWe find little evidence that snowfall will compensate enhanced ice sheet melt due to climate warming

Knowing the extent of human influence on the global hydrological cycle is essential for the sustainability of freshwater resources on Earth 1 , 2 . However, a lack of water level observations for the world’s ponds, lakes and reservoirs has limited the quantification of human-managed (reservoir) changes in surface water storage compared to its natural variability 3 . The global storage variability in surface water bodies and the extent to which it is altered by humans therefore remain unknown. Here we show that 61% per cent of the Earth’s seasonal surface water storage variability occurs in human-managed reservoirs. Using measurements from NASA’s ICESat-2 satellite laser altimeter, which was launched in late 2018, we assemble an extensive global water level dataset that quantifies water level variability for 227,386 water bodies from October 2018 to July 2020. We find that seasonal variability in human-managed reservoirs averages 0.86 metres, whereas natural water bodies vary by only 0.22 metres. Natural variability in surface water storage is greatest in tropical basins, whereas human-managed variability is greatest in the Middle East, southern Africa and the western USA. Strong regional patterns are also found, with human influence driving 67 per cent of surface water storage variability south of 45 degrees north and nearly 100 per cent in certain arid and semi-arid regions. As economic development, population growth and climate change continue to pressure global water resources 4 , our approach provides a useful baseline from which ICESat-2 and future satellite missions will be able to track human modifications to the global hydrologic cycle.

This dataset contains water level records derived from ICESat-2 for 227,386 lakes spanning Oct 14, 2018 to July 16, 2020. These records have been updated to reflect an error in the calculation of lake area in the previous version which led to an overestimation of lake area at high latitudes. For details on how this correction was performed, please see the 2023 Addenda to Cooley et al (2021). A complete description of the method used to derive water level from ICESat-2 can be found in Cooley et al (2021), but is briefly summarized below: We create a conservative water mask modified from the Global Surface Water Occurrence (GSWO) product (Pekel et al., 2016); We intersect ATL08 mean terrain height returns with this water mask, requiring water bodies to receive at least three ICESat-2 point observations on the same day to be included in the analysis; We filter observations based on the mean standard deviation of returns, among other factors; We aggregate observations to monthly timesteps; We calculate seasonal variability in water level as the maximum minus the minimum monthly water level over the 22-month period. This dataset contains: ICESat2_lake_variability_v2_updated.shp: A shapefile containing lake points and summary statistics (i.e. height variability, storage variability, etc) *updated to include corrected lake area and storage values ICESat2_lake_height_time_series_v2_updated.csv: A csv file of the monthly water height time series used to calculate the global lake level variability *updated to include corrected lake area values 265 water mask GeoTiffs: Water masks created from GSWO which we intersect with ICESat-2 data to produce the water level time series *unchanged from previous version ICESat2_mask_reference_v2_updated.csv – A csv file which lists the corresponding water mask for each water body in the dataset *updated to include corrected lake area values USGS_height_validation_v2_updated.csv – A csv file containing the height comparison between ICESat-2 and USGS gauges used for validation and uncertainty analyses *updated to include corrected lake area values USGS_range_validation_ v2_updated.csv – A csv file containing the range comparison between ICESat-2 and USGS gauges used for validation and uncertainty analyses *updated to include corrected lake area values California_storage_validation_v2_updated.csv – A csv file containing the storage comparison between ICESat-2 and California Department of Water Resource gauges used for validation and uncertainty analyses *updated to include corrected lake area and storage values See the README file for a more detailed description of this dataset. Anyone wishing to use this dataset should cite Cooley et al. 2021) and contact Sarah Cooley at scooley2@uoregon.edu with a description of the work and any questions so that we may offer guidance in regards to the best usage of our dataset. Cooley, S.W., Ryan, J.C., and Smith, L.C., (2021), Human alteration of global surface water storage variability, Nature, https://doi.org/10.1038/s41586-021-03262-3

Meltwater runoff from the Greenland ice sheet (GrIS) is an important contributor to global sea level rise, but substantial uncertainty exists in its measurement and prediction. Common approaches for estimating ice sheet runoff are in situ gauging of proglacial rivers draining the ice sheet and surface mass balance (SMB) modeling. To obtain hydrological and meteorological data sets suitable for both runoff stage characterization and, pending the establishment of stage–discharge curves, SMB model evaluation, we established an automated weather station (AWS) and a cluster of traditional and experimental river stage sensors on the Minturn River, the largest proglacial river draining Inglefield Land, NW Greenland. Secondary installations measuring river stage were installed in the Fox Canyon River and North River at Pituffik Space Base, NW Greenland. Proglacial runoff at these sites is dominated by supraglacial processes only, uniquely advantaging them for SMB studies. The three installations provide rare hydrological time series and an opportunity to evaluate experimental measurements of river stage from a harsh, little-studied polar region. The installed instruments include submerged vented and non-vented pressure transducers, a bubbler sensor, experimental bank-mounted laser rangefinders, and time-lapse cameras. The first 3 years of observations (2019 to 2021) from these stations indicate (a) a meltwater runoff season from late June to late August/early September that is roughly synchronous throughout the region; (b) the early onset (∼ 23 June to 8 July) of a strong diurnal runoff signal in 2019 and 2020, suggesting minimal meltwater storage in snow and/or firn; (c) 1 d lagged air temperature that displays the strongest correlation with river stage; (d) river stage that correlates more strongly with ablation zone albedo than with net radiation; and (e) the late-summer rain-on-ice events appear to trigger the region's sharpest and largest floods. The new gauging stations provide valuable in situ hydrological observations that are freely available through the PROMICE network (https://promice.org/weather-stations/, last access: 14 September 2023).

This dataset contains a landfast ice climatology calculated from MODIS imagery. For a complete description of the methods, please see Cooley and Ryan (forthcoming). This dataset contains a shapefile "landfast_ice_edge_2000_2022.shp" of maximum ice edge positions derived from MODIS imagery. The 'time period' column refers to the 30-day period over which the edge position was calculated, specifically 1: March 1st to April 1st, 2: March 15th to April 15th and 3: April 1st to May 1st. For years with multiple lines and/or multiple time periods present in the dataset, we retain the maximum position in our analysis; in other words, we calculate the ice edge position based on the furthest line from the coast. This dataset also contains time series of ice edge position (distance to the ice edge from the coastline, in km) and breakup timing. The ice edge position is calculated as the maximum ice edge distance from coastline using the ice edge shapefile described above. The breakup date is defined as the day when 75% of the area contained within the maximum ice edge position for that year reaches open water. This is corrected for cloud cover by assigning the actual breakup date as the midpoint between the first day when the ice was open water and the previous cloud-free observation. This uncertainty (in days) is recorded as "breakup_uncertainty". Any usage of this data should cite Cooley and Ryan (forthcoming). For any questions about the dataset, please reach out to Sarah Cooley at scooley2@uoregon.edu

Abstract. Meltwater runoff from the Greenland Ice Sheet (GrIS) is an important contributor to global sea level rise, but substantial uncertainty exists in its measurement and prediction. Common approaches for estimating ice sheet runoff are in situ gauging of proglacial rivers draining the ice sheet, and surface mass balance (SMB) modeling. To obtain hydrological and meteorological datasets suitable for both runoff characterization and SMB model validation, we established an automated weather station (AWS) and cluster of traditional and experimental river stage sensors on the Minturn River, the largest proglacial river draining Inglefield Land, NW Greenland. Secondary installations measuring river stage were installed in the Fox Canyon River and North River at Thule Air Base, NW Greenland. Proglacial runoff at these sites is dominated by supraglacial processes only, uniquely advantaging them for SMB studies. The three installations provide rare hydrological time-series and an opportunity to evaluate experimental measurements of river stage from a harsh, little-studied polar region. The installed instruments include submerged vented and non-vented pressure transducers, a bubbler sensor, experimental bank-mounted laser rangefinders, and time-lapse cameras. The first three years of observations (2019 to 2021) from these stations indicate a) a meltwater runoff season from late June to late August/early September, roughly synchronous throughout the region; b) early onset (~ June 23 to July 8) of a strong diurnal runoff signal in 2019 and 2020, suggesting minimal meltwater storage in snow/firn; c) one-day lagged air temperature displays the strongest correlation with river stage; d) river stage correlates more strongly with ablation zone albedo than with net radiation; and e) late-summer rain-on-ice events appear to trigger the region’s sharpest and largest floods. The new gauging stations provide valuable in situ hydrological observations from a little-studied, rapidly changing area and are freely available through the PROMICE network (https://promice.org/weather-stations/).

The Greenland Ice Sheet is a leading source of global sea level rise, due to surface meltwater runoff and glacier calving. However, given a scarcity of proglacial river gauge measurements, ice sheet runoff remains poorly quantified. This lack of in situ observations is particularly acute in Northwest Greenland, a remote area releasing significant runoff and where traditional river gauging is exceptionally challenging. Here, we demonstrate that georectified time-lapse camera images accurately retrieve stage fluctuations of the proglacial Minturn River, Inglefield Land, over a 3 year study period. Camera images discern the river’s wetted shoreline position, and a terrestrial LiDAR scanner (TLS) scan of riverbank microtopography enables georectification of these positions to vertical estimates of river stage. This non-contact approach captures seasonal, diurnal, and episodic runoff draining a large (∼2,800 km 2 ) lobe of grounded ice at Inglefield Land with good accuracy relative to traditional in situ bubble-gauge measurements ( r 2 = 0.81, Root Mean Square Error (RMSE) ±0.185 m for image collection at 3-h frequency; r 2 = 0.92, RMSE ±0.109 m for resampled average daily frequency). Furthermore, camera images effectively supplement other instrument data gaps during icy and/or low flow conditions, which challenge bubble-gauges and other contact-based instruments. This benefit alone extends the effective seasonal hydrological monitoring period by ∼2–4 weeks each year for the Minturn River. We conclude that low-cost, non-contact time-lapse camera methods offer good promise for monitoring proglacial meltwater runoff from the Greenland Ice Sheet and other harsh polar environments.

We present a portable photon-counting LiDAR that uses a bistatic geometry to measure pulse broadening in the multiple-scattering regime. A diffusion model allows us to extract optical scattering and absorption coefficients of glacier ice.

Clouds regulate the Greenland Ice Sheet’s surface energy balance through the competing effects of shortwave radiation shading and longwave radiation trapping. However, the relative importance of these effects within Greenland’s narrow ablation zone, where nearly all meltwater runoff is produced, remains poorly quantified. Here we use machine learning to merge MODIS, CloudSat, and CALIPSO satellite observations to produce a high-resolution cloud radiative effect product. For the period 2003–2020, we find that a 1% change in cloudiness has little effect (±0.16 W m −2 ) on summer net radiative fluxes in the ablation zone because the warming and cooling effects of clouds compensate. However, by 2100 (SSP5-8.5 scenario), radiative fluxes in the ablation zone will become more than twice as sensitive (±0.39 W m −2 ) to changes in cloudiness due to reduced surface albedo. Accurate representation of clouds will therefore become increasingly important for forecasting the Greenland Ice Sheet’s contribution to global sea-level rise.

Constraining the optical properties of glacier ice and snow is required for accurate forecasts of sea levels and water resources. Here we present two active sensing techniques for measuring the optical properties of snow and ice which can be used to inform models.

Abstract The production of meltwater from glacier ice, which is exposed at the margins of land ice during the summer, is responsible for a large proportion of glacier mass loss. The rate of meltwater production from glacier ice is especially sensitive to its physical structure and chemical composition which combine to determine the albedo of glacier ice. However, the optical properties of near-surface glacier ice are not well known since most prior work has focused on laboratory-grown ice or deep cores. Here, we demonstrate a measurement technique based on diffuse propagation of nanosecond-duration laser pulses in near-surface glacier ice that enables the independent measurement of the scattering and absorption coefficients, allowing for a complete description of the processes governing radiative transfer. We employ a photon-counting detector to overcome the high losses associated with diffuse optics. The instrument is highly portable and rugged, making it optimally suited for deployment in remote regions. A set of measurements taken on Crook and Collier Glaciers, Oregon, serves as a demonstration of the technique. These measurements provide insight into both physical structure and composition of near-surface glacier ice and open new avenues for the analysis of light-absorbing impurities and remote sensing of the cryosphere.

Ice surface albedo is a primary modulator of melt and runoff, yet our understanding of how reflectance varies over time across the Greenland Ice Sheet remains poor. This is due to a disconnect between point or transect scale albedo sampling and the coarser spatial, spectral and/or temporal resolutions of available satellite products. Here, we present time-series of bare-ice surface reflectance data that span a range of length scales, from the 500 m for Moderate Resolution Imaging Spectrometer’s MOD10A1 product, to 10 m for Sentinel-2 imagery, 0.1 m spot measurements from ground-based field spectrometry, and 2.5 cm from uncrewed aerial drone imagery. Our results reveal broad similarities in seasonal patterns in bare-ice reflectance, but further analysis identifies short-term dynamics in reflectance distribution that are unique to each dataset. Using these distributions, we demonstrate that areal mean reflectance is the primary control on local ablation rates, and that the spatial distribution of specific ice types and impurities is secondary. Given the rapid changes in mean reflectance observed in the datasets presented, we propose that albedo parameterizations can be improved by (i) quantitative assessment of the representativeness of time-averaged reflectance data products, and, (ii) using temporally-resolved functions to describe the variability in impurity distribution at daily time-scales. We conclude that the regional melt model performance may not be optimally improved by increased spatial resolution and the incorporation of sub-pixel heterogeneity, but instead, should focus on the temporal dynamics of bare-ice albedo.